Operations | Monitoring | ITSM | DevOps | Cloud

Technical proposals can be like campfire stories. You start at the beginning, walk through every discovery & technical detail, and eventually arrive at the point. The problem? By then, you've lost the audience.

A golden path for dependency management isn't a policy document – it's a preconfigured private registry with upstream proxies covering every ecosystem your teams use, set as the default. Developers don't opt into security; they get it automatically by using the standard toolchain. The alternative is teams configuring their own controls, producing inconsistent postures and compounding risk across the org. If the secure path requires extra steps, developers will route around it. Make it the easiest option and the policy enforces itself.

Is your infrastructure as secure as you think it is? In this walkthrough, see how aSystem Hardening Assessmenthelps organizations identify security gaps, uncover configuration risks, and prioritize remediation efforts across critical systems. You'll learn how teams can evaluate their environment against security best practices, gain visibility into potential vulnerabilities, and take actionable steps to strengthen their overall security posture.

Logic App costs can be tricky - connectors, triggers, executions, and the consumption vs. standard plan decision all affect what you pay. Turbo360's Logic App Cost Review agent gives integration teams an instant AI-powered breakdown of cost patterns, what's driving the bill, and whether you're on the right plan. What you'll see.

Handed over an Azure integration environment you've never seen before? Turbo360's AI Resource Summary agent gives any support operator or engineer an instant plain-English overview of what a resource is, how it behaves, and what to watch out for - without needing to ask the developers. In this demo: Great for: IT operations teams, MSP NOCs, cloud support engineers, and anyone responsible for running integration workloads they didn't build.

Enterprise AI adoption is accelerating, but are organizations actually solving business problems or just adding more tools? In this episode of Agents of IT, Fran Fernandez (Chief Product Officer at Resolve) and Zach Austin (Director of Product Marketing) explore one of the biggest challenges facing enterprise IT in 2026: AI tool sprawl. They discuss why many organizations struggle to demonstrate ROI from AI investments, how disconnected AI assistants create operational complexity, and why orchestration, automation, and context have become the real differentiators for enterprise AI success.

When a malicious package is first published, threat intelligence sources haven't flagged it yet – and every team pulling from a public registry is exposed during that entire window. The fix isn't faster scanning; it's a policy that holds new packages for a defined cooldown period before they're eligible to pull. By the time the window closes, the threat intelligence has caught up. Teams pulling direct from npm or PyPI have no equivalent enforcement layer – which is exactly how attacks like Shai-Hulud got in.

Today, we're launching Autonomous Worker Agents, AI agents that run as governed pipeline steps inside Harness. They inherit OPA policies, RBAC, audit trails, and scoped credentials from the first run. And because they live inside your Harness pipelines, they reason using the Harness Knowledge Graph: your services, deployments, incidents, and policies.

AI agents write broken code nearly 50% of the time. By adding a traffic-based deterministic evaluation, Speedscale boosted unsupervised bug-fixing quality from 51% to 77% in just 5 minutes. This helped slash token costs and eliminate rework without human intervention. Learn more: speedscale.com.

Tired of outage after outage? Use traffic replay to automatically generate regression tests for your CI pipeline in just a few clicks. Take back your time! Read more at speedscale.com.

Same repo. Same tasks. Same validation gates. The only difference? Where validation happened. Here's how Chunk sidecars returned feedback in 22 seconds versus about 69 seconds through a traditional CI pipeline, giving the agent enough time to catch the failure, fix it, and revalidate before pushing.

Autonomous IT starts now with LogicMonitor and Edwin AI, built to help IT teams monitor complex hybrid IT environments, discover root cause faster, reduce downtime, and prevent incidents before they impact revenue or brand reputation. See how LogicMonitor brings AI-powered IT operations, observability, and incident prevention together for modern infrastructure teams.

Monitoring your DigitalOcean infrastructure just got easier. MetricFire now integrates natively with DigitalOcean, so you can connect your account and start streaming metrics from Droplets, Load Balancers, Managed Databases, and more directly into Grafana. No agents. No setup overhead. No dashboard stitching. Get full visibility into your DigitalOcean infrastructure from one dashboard, live in minutes.

• Disaster recovery planning is essential for ensuring digital services remain online in the face of catastrophic failures or outages. When a major digital infrastructure outage occurs, systems need to be set up to automatically respond and restore functionality as quickly as possible. But no matter how in-depth your disaster recovery plan is, it’s still only theoretical until it’s thoroughly tested under realistic failure conditions, which is why testing is often mandated by leadership and regulators.

A major platform modernization bringing Java 17, Spring Boot 3, native Prometheus monitoring, and meaningful performance and security improvements to your automation stack.#AIOps.

95% of Companies See No AI ROI. Here's Why | AI Tool Sprawl Explained 88% of companies are using AI, yet 95% report no measurable ROI. In this short from Agents of IT, we discuss why AI tool sprawl, poor workflow design, and lack of orchestration may be preventing enterprises from realizing the value of AI.